Questions in topic: "imputation"
https://developer.ibm.com/answers/questions/topics/single/241777.html
The latest questions for the topic "imputation"Help Computing Variable
https://developer.ibm.com/answers/questions/484490/help-computing-variable.html
I am trying to compute two variables into 1. Both variables are discreet values between 1 to 5. (i.e. they are 1, 2, 3, 4 or 5).
For some reason whenever I try to compute Variable 1 with Variable 2 I just get blank values in the output variable.
My formula looks like this: Target Variable = Variable1 + Variable 2
The data doesn't overlap. So where there's a value for Variable 1, there's no value for Variable 2.
I could do it manually, but that would take forever...
I am able to compute other variables, for instance ones that are scales between 0-100, etc.spssstudenttransformationimputationTue, 11 Dec 2018 19:44:25 GMTpolitesheepKaplan Meier interpretation following multiple imputation
https://developer.ibm.com/answers/questions/483543/kaplan-meier-interpretation-following-multiple-imp.html
Hello,
I have conducted multiple imputation my dataset, and now I am doing survival analysis, starting with Kaplan Meier. I am running into a few issues:
1) I am receiving Means, standard error, and CI for all imputations, but only receiving Means and SE for the pooled outcome. Of course I can calculate CI manually with that information, I am just wondering if there is an easy way to ensure I receive CIs for my pooled means as well.
2) This one is more critical... I am not receiving a pooled log-rank outcome (chi square + p-value)- only separate ones for each imputation. Is there a way to receive the pooled outcome with SPSS? Or is there a method to calculating it manually (example, medians?)
Thank you!
TalispssstudentimputationsurvivalTue, 04 Dec 2018 16:42:06 GMTTali S.I split my data file by gender before conducting multiple imputation.
https://developer.ibm.com/answers/questions/481891/i-split-my-data-file-by-gender-before-conducting-m.html
I split my file by gender before conducting multiple imputation with my data. This results in me getting one datafile with multiple imputed data, as if I did not split the file. In the output however, it does show imputed values separately for men and women. I cannot tell if the multiple imputation was done separately for the men and women for the sample, or if the split file command was ignored when doing the multiple imputation. I would like the multiple imputation to occur separately for men and women because I will examine gender as a moderating variable.imputationsplitSat, 24 Nov 2018 00:05:04 GMTssdermodyCan you do syntax for median imputation with missing data?
https://developer.ibm.com/answers/questions/475462/can-you-do-syntax-for-median-imputation-with-missi.html
Can you do syntax for median imputation with missing data?syntaxmissingimputationMon, 15 Oct 2018 15:18:55 GMTRachelAubryWhat can I do to impute data in this new version as I only see compute variable function under the transform command?
https://developer.ibm.com/answers/questions/462840/what-can-i-do-to-impute-data-in-this-new-version-a.html
What can I do to impute data in this new version as I only see compute variable function under the transform command? I am aware of the older version whereby, I was able to use the random number generator command.imputationspss-statistics-previewtransformMon, 06 Aug 2018 02:45:09 GMTSPSS EXPLORATION NEW VERSIONSPSS Estimation Maximization (EM) Test Issue
https://developer.ibm.com/answers/questions/456557/spss-estimation-maximization-em-test-issue.html
I am having trouble imputing values into my dataset.
I have 'cleaned up' my data including eliminating participants that are not from the population I am testing from the sample and participants who left all of the questions in my survey blank for extreme missing data. I then ran Little's MCAR test which revealed that my data is happily missing completely at random. I then decided to eliminate cases that left 20% or more of the questions blank. I then attempted to run the Expectation Maximization (EM) test to replace the remaining missing values, but am running into some trouble.
I did this by going to the Analyze tab followed by Missing Values Analysis. I moved the variables associated with the subscale into the quantitative/categorical variables boxes. I put the case ID numbers into the Case Label box. Under estimation, I selected EM. I then clicked EM and saved the new dataset to a new name. I clicked OK. A new dataset was created, but the missing values remain.
I was careful to categorize each of the variables accordingly, either as quantitative of categorical. I also did not include all of my variables at once, but rather only about 8-10 at a time to create multiple sub scales I intended to merge together. It is still not imputing the missing values.
What can I do to correct this? Can you please walk me through how to complete the EM test?spssstudenterrorimputationanalyzedatasetreplaceTue, 03 Jul 2018 15:35:22 GMTMir7SPSS Expectation Maximization (EM) Test Trouble
https://developer.ibm.com/answers/questions/456063/spss-expectation-maximization-em-test-trouble.html
Hi,
I am having trouble imputing values into my dataset.
I have 'cleaned up' my data including eliminating participants that are not from the population I am testing from the sample and participants who left all of the questions in my survey blank for extreme missing data. I then ran Little's MCAR test which revealed that my data is happily missing completely at random. I then decided to eliminate cases that left 20% or more of the questions blank. I then attempted to run the Expectation Maximization (EM) test to replace the remaining missing values, but am running into some trouble.
I did this by going to the Analyze tab followed by Missing Values Analysis. I moved the variables associated with the subscale into the quantitative/categorical variables boxes. I put the case ID numbers into the Case Label box. Under estimation, I selected EM. I then clicked EM and saved the new dataset to a new name. I clicked OK. A new dataset was created, but the missing values remain.
I was careful to categorize each of the variables accordingly, either as quantitative of categorical. I also did not include all of my variables at once, but rather only about 8-10 at a time to create multiple sub scales I intended to merge together. It is still not imputing the missing values.
What can I do to correct this? Can you please walk me through how to complete the EM test?
Looking forward to hearing from you.spssspssstudenterrorimputationanalyzedatasetreplaceFri, 29 Jun 2018 22:39:01 GMTMir7Repeated Measures ANOVA with multiple imputation data
https://developer.ibm.com/answers/questions/451151/repeated-measures-anova-with-multiple-imputation-d.html
Does SPSS version 24 provide pooled results for repeated measures ANOVA performed on imputed data? I know that this option was not available in version 17.statisticsanovaimputationFri, 01 Jun 2018 16:13:02 GMTAwesomewonder83How to JOINTLY perform multiple imputation with normal dataset and multiple response sets in SPSS? And right kind of Cluster analysis for binary data?
https://developer.ibm.com/answers/questions/446280/how-to-jointly-perform-multiple-imputation-with-no.html
Here you have my attachment. Please open it
[link text][1]
Please im begging anybody’s help because im an mba student in global hospitality and tourism management and my teacher is unable to help I have purchase the spss premium version so I can use all its features I carried out a survey of Chinese tourists at the airport in order to perform pattern recognition and analysis about them, so I created this small-sized database whose cases are 130 tourists which departed from Milano, Italy in December. There is quite a number of variables Here you have the attached file, my dataset, made from multiple sheets. The first one if the raw datasheet, with variables not split. The second one is the main file actually supposed to be imported into spss. The remaining are multiple response data sets which cannot can be imported as well, but I want them to me analyzed JOINTLY alongside the main sheet. I wish I could merge the sheets into a single sheet to be entered into spss, I have 6 multiple response sets, whose missing values have to be imputed through multiple imputation indeed, but I need to bind the results and the relationships to the main dataset which will be multiply imputed according to the normal spss procedure. They will have to be dummy coded into spss I believe
Ive browsed the web all over searching for papers to help me, i even red lots of discussions on this, it took me more than 1 month to read all of it and for subsequently analyzing those. But so found I found nothing helpful in my case. I have almost no background in statistics that’s why I’m struggling I wish I could merge the sheets into a single sheet to be entered into spss, or at least find a way to bind the algorithm so as to include all my sheets and the procedure to take into account PATTERN At first I thought I had to use subscales, but then I found out that these do refer do multiple non-adjacent items, actually belonging to different questions Does anybody know of any string or manually entered code which will allow me to do this? Or a way to enter the excel sheet into spss or to merge them? How to set up the spreadsheet? If not the multiple imputation will think that different excel sheets are different datasets when they are actually the same I’m afraid these data are not jointly analyzable, I really hope to be wrong, otherwise this will result in data loss on my part
I’ve red these paper which seem not to be helping
-Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation
-Multiple Imputation by Chained Equations: What is it and how does it work? And on top of that, I can’t find this paper online and authors are unreponsive
-A Comparison of Item-Level and Scale-Level Multiple Imputation for Questionnaire Batteries
So I supposed that I should go on analyzing the main data set which is made from nominal, scale, dichotomous and ordinal variables.
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After the data being solved that I will have to run the multiple imputation itself, but perhaps another kind of analysis is best here. Perhaps factor analysis, but it seems to me that its actually useful for classify variables, not cases Does missing value analysis tell me whether the data are MCAR or MAR MNAR? An user pointed out that data at the end of the day there is always some pattern , and thus no data are truly missing at random If the data are MCAR then listwise deletion is a feasible option, but in order to tell that I need a missing value analysis first. But if the data on the other hand are MAR, then the expectation-maximization could be just as good as multiple imputation. But my teacher told me to perform cluster analysis
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After that I will run descriptive statistics, and afterwards I will test the null hypothesis to check whether some hypothesis I will have developed hold true. This will be a lengthy process I guess I’m unsure whether I have to weight cases, but I red it must be done. Perhaps some variables hold a greater weight than others, but im clueless on how to continue. Please help me in this too. It After that I will have to set the right kind of cluster analysis (since these multiple response sets are indifferent excel sheets), which I reckon can’t jointly be performed on the first dataset either, but just like the multiple imputation it needs to. The same problem explained in the first heading above And also, im unsure on what kind of spss cluster analysis to use, since you can see my data are binary, categorical, nominal and more. It seems to me that k-means clustering can only handle scale/continuous data and is therefore useless here. So it’s either hierarchical clustering or 2-step clustering Hierarchical or TwoStep cluster analysis for binary data?? As this knowledgeable spss developed pointed out, despite what spss website spss, the hierarchical cluster analysis is actually good for binary data I fail to understand the difference between nominal binary and ordinal binary data he points out, which according to him call for different actions, but you can see that since ordinal binary data like mine cannot be analyzed by the 2-step clustering solution, then I don’t know what to do
And what variables should I use for clustering? Will weighting cases affect them? Lastly, I will have to make sure my clusters will be valid, and what should I do for knowing that? If anyone among you professionals could be so kind as to help me out it would be great and, for what is worth it, mentioned in the acknowledgements section of my thesis I’m even willing to hire an expert among you to help me get out this problem, it’s been more than 2 months since im stuck in this situation I’m pretty confused and I therefore ask for people to help me, the procedure is lengthy and I need to graduate
All the best
[1]: /answers/storage/temp/22004-add-on.zipspssclusteranalysisclusteringimputationSat, 05 May 2018 21:11:28 GMTErasminopooling algorithms in multiple linear regression
https://developer.ibm.com/answers/questions/425663/pooling-algorithms-in-multiple-linear-regression.html
Are Rubin's rules used as pooling algorithms in multiple linear regression with multiple imputation dataset by SPSS. If not, could you tell me what rule is applied ?statisticsrulesregressionalgorithmsimputationFri, 19 Jan 2018 14:50:53 GMTamachanProblem with SPSS multiple imputation
https://developer.ibm.com/answers/questions/424881/problem-with-spss-multiple-imputation.html
Does anyone have a suggestion for how to proceed when getting this message. I've tried increasing the number of draws with no success.
Warnings
After 4000 draws, the imputation algorithm cannot find an imputed value under the constraints for variable X. Check the minimum and maximum values specified to determine that they are reasonable, and consider raising the number of draws allowed.
Execution of this command stops.imputationTue, 16 Jan 2018 20:48:15 GMTDianneLCAdding a new variable to an imputed dataset
https://developer.ibm.com/answers/questions/422621/adding-a-new-variable-to-an-imputed-dataset.html
Hi all,
I have a question related to adding variables to imputed datasets. I have an imputed dataset, which means that each case appears 10 times in the dataset (we performed multiple imputation and did 10 imputations). Now I want to add an extra variable to this imputed dataset. Is that possible?
I tried multiple times by using the key variable ID (each case has a unique ID), but then, for each case, a value for the new variable was only added to the first imputed dataset. No value was added to the other 9 datasets of that case. So it seems not to work.
I hope anyone can help! Thank you very much in advance!spssmergeimputationSat, 06 Jan 2018 12:12:07 GMTLinda.How to deal with Multiple imputation for big data in SPSS 23?
https://developer.ibm.com/answers/questions/414525/how-to-deal-with-multiple-imputation-for-big-data.html
I am trying to conduct multiple imputation for variable with 35% missing in my 2,000,000 cases data set in SPSS 23. I have received the error: "The model cannot be built because a computational error has occurred during the estimation. No output will be displayed." All the other variables are not imputed. Thanks for your helpimputationWed, 22 Nov 2017 08:13:06 GMTkern.agay.shayMultiple imputation error
https://developer.ibm.com/answers/questions/414157/multiple-imputation-error.html
I tried to run a multinomial regression using imputed values but it says "for at least one model, pooled estimates could not be computed because model parameters vary by imputation." Is there anything I can do to fix this?
I tried to run a multinomial regression using imputed values but it says "for at least one model, pooled estimates could not be computed because model parameters vary by imputation." Is there anything I can do to fix this?regressionimputationMon, 20 Nov 2017 18:18:16 GMTHannahMGHow can SPSS recognize a dataset imputed on another package as imputed
https://developer.ibm.com/answers/questions/412621/how-can-spss-recognize-a-dataset-imputed-on-anothe.html
I have imputed my data, which is multilevel, on Mplus and combined all the iterations to form a dataset and transferred to SPSS.
but I am not sure how to make SPSS recognize this dataset as imputed with multiple iterations.
I have tried splitting the file, but the problem is I cant get a pooled analysis of the results from all imputations this way.
anyone got an idea how I can beat this one?spssstatisticsimputationMon, 13 Nov 2017 12:49:29 GMTabcdefgijReinstalled my SPSS 23 and still no Multiple Imputation option from the drop-down list when I click on the Analyze tab;
https://developer.ibm.com/answers/questions/381578/reinstalled-my-spss-23-and-still-no-multiple-imput.html
...also no "Missing Value Analysis, Loglinear, Survival, Complex Samples" optionsmultiplebuttonimputationWed, 14 Jun 2017 20:30:34 GMT17YemiHow to use a dataset with multiple imputations in AMOS?
https://developer.ibm.com/answers/questions/372348/how-to-use-a-dataset-with-multiple-imputations-in.html
I created a file in SPSS with multiple imputations (because of missing data). I did 5 imputations. Now I want to analyze this datafile in AMOS. However, AMOS is showing the results of the 5 datasets, but there are no results showed for the pooled data (like SPSS does for some types of analyses).
Can I use a dataset with multiple imputations in AMOS or is this not possible?
Is there a way to distract only pooled data from SPSS?spssamosimputationTue, 02 May 2017 18:03:41 GMTSnezanaHow to condition multiple imputation model to predict time 2 values from time 1 values (in long format)?
https://developer.ibm.com/answers/questions/360992/how-to-condition-multiple-imputation-model-to-pred.html
Dear users,
I am analysing data from an intervention study with 2 time points (baseline and follow-up) and 3 groups. The data is in a long format. As I have 25% of missing data at follow-up on my outcomes (reaction time and accuracy on cognitive tasks), I used multiple imputation technique to address the missing data.
The data is missing at random, however more than half of my variables (RTs) are not normally distributed. I am thus using Predictive Mean Matching.
My multiple imputation model contains 46 variables including categorical and continuous covariates including BMI. I would like to condition my multiple imputation model to account for baseline values in calculating the ones for follow up. However, running the model on split dataset (by time (baseline and follow-up) and group (control and intervention 1 and intervention 2) does not address this issue. For example, the values imputed for BMI can differ by as much as 20 kg/m2 from baseline which is unfeasible.
I therefore wondered if it is possible to specify this constraint in spss prior to running multiple imputation?
Thank you very much in advance for your time and consideration.
Kind regards,
dmp38imputationWed, 08 Mar 2017 23:40:58 GMTdmp38What is the regression imputation method ?
https://developer.ibm.com/answers/questions/320198/what-is-the-regression-imputation-method.html
I can use the regression imputation method in AMOS to imputate potential variables. But I don' know the principles of this method. Is there anybody who can introduce this method , or tell me where I can study those contents. Thank you!!!amosimputationMon, 14 Nov 2016 03:15:46 GMTYangSirLack of Missing Values Diagnostics for Logistic Regression Solution to MI
https://developer.ibm.com/answers/questions/318029/lack-of-missing-values-diagnostics-for-logistic-re.html
I've been asked to run diagnostic tests (trace plots) with my data, but since the imputed variables are dichotomous (logistic regression solution), no iteration history output is produced. Getting around this, there are 5 variables for a much larger set of variables that I want to analyze which require MI due to the researchers’ decision to impute for greater than 5% missing. I'm creating 5 datasets for these 5 variables, each with a somewhat overlapping, but different set of 3 or 4 predictors that best correlate with these variables, and with their missingness. (I would think this should make it very unlikely that I will have diagnostic problems with the 30 imputations selected per each imputed variable, although I do wish there were some diagnostic tests!) Assuming this is correct logic, when I conduct my final multivariate analysis - binary logistic regression, do I need to run another MI for all of the variables requiring imputations in one large MI run, or can I merge in the "separate" MI variables from the separate MI runs done previously? (I see that I can do physically do this merge and get the pooled results for each variable within one file, but don't know if this is methodologically advisable -- how does this strategy sound to you?)missinglogisticregressiondiagnosticsimputationThu, 03 Nov 2016 14:34:14 GMTGKSPSSMultiple Imputation button missing from SPSS 24
https://developer.ibm.com/answers/questions/299941/multiple-imputation-button-missing-from-spss-24.html
I just purchased a IBM® SPSS® Statistics Standard GradPack 24 for Mac from OnTheHub. I am trying to do multiple imputation method for missing values, but when I go to Analyze there is no Multiple Imputation button. I saw the previous answer stating that it may be because I don't have a valid license for the Missing Values module. I ran the suggested command show lic. in the new Syntax window and it shows that I have license for IBM SPSS Statistics, Regression, Advanced Statistics and Statistics Base. Which license am I missing and how can I get it? I thought the Advanced Statistics package should provide me with such basic tool. Is there another reason why I don't see the Multiple Imputation button? Thank you!multiplebuttonimputationWed, 31 Aug 2016 22:04:26 GMTOxanaWhat specific method does AMOS use to impute missing data when Stochastic Regression is selected and 10 datasets are produced...
https://developer.ibm.com/answers/questions/287431/what-specific-method-does-amos-use-to-impute-missi.html
I created a diagram in amos of 200 interval ratio scaled variables with associated error terms. I had to create a observed variable for each item so that the missing values among all 200 items would be imputed. I have no real path model structure or 'dependent' variable. However, I was able to produce a series of imputed datasets, filling in the values for all 200 items this way.
My more immediate question is whether or not the imputation used when stochastic regression is selected falls under the category of:
EM (Expectation Maximization)
MI (Multiple Imputation)
And / Or
FIML (Full Information maximum likelihood)
I understand that there are differences between these methods but I am unsure what method AMOS is exactly using. My goal was to use FIML, but I fear what resulted was instead, some sort of MI using stochastic regression. The obvious follow-up question to this would be. How do I employ FIML (without an actual path model) in my scenario, to produce estimates for missing values.spssamosmissingimputationfimlTue, 12 Jul 2016 18:51:44 GMTThomasLofaroPooled standard deviations in multiple imputation dataset
https://developer.ibm.com/answers/questions/267044/pooled-standard-deviations-in-multiple-imputation.html
I have a dataset where I have used multiple imputation to generate 20 imputed datasets. When calculating means and standard deviations, SPSS 21 only gives me pooled outcomes for means and not for standard deviations. Does anyone know whether SPSS 21 can give me pooled outcomes for standard deviations as well?imputationWed, 27 Apr 2016 19:23:23 GMTJoarHalvorsenunable to recode into different variables or compute new variables in multiply imputated dataset
https://developer.ibm.com/answers/questions/260978/unable-to-recode-into-different-variables-or-compu.html
I've successfully created a new dataset using multiple imputation. However, I'm unable to transform imputed scores into new scores; they read as missing in the data view of the transformed variable. (Box has a dot and nothing else in it.) I have double checked to be sure the level of measure is correct for the imputed variables and that the imputed values are plausible. SPSS seems to be treating them as missing even after they have been successfully imputed, so I am unable to recode using the "Recode into Different Variables" command although all my other non-missing, non-imputed data for that variable is successfully recoded. Thanks for any help you can offer. I am using version 23.0.0.0 on a Mac with Yosemite OS.spssstudenttransformationimputationWed, 30 Mar 2016 12:34:45 GMTLoraxHow do I get R Square and Adjusted R square for pooled data?
https://developer.ibm.com/answers/questions/241778/how-do-i-get-r-square-and-adjusted-r-square-for-po.html
Dear all, we have a dataset with imputed data. We can not get the estimates for R Square and Adjusted R Square for the pooled values. How do we get those? Any ideas? Thanks a lot! Juliahow-torimputationWed, 02 Dec 2015 15:12:23 GMTJuliaTUD